Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.

ABSTRACT: Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history. The authors focused on extreme (≥40%) missing at random of key features which depend on AD progression; identified as the history of a mother having AD, APoE ε4 alleles, and clinical dementia rating. Along with features selected using traditional feature s

SUBMITTER: Haridas NT 

PROVIDER: S-EPMC11665783 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature